Systems and methods for predicting perfusion deficits from physiological, anatomical, and patient characteristics

ABSTRACT

Systems and methods are disclosed for using patient specific anatomical models and physiological parameters to estimate perfusion of a target tissue to guide diagnosis or treatment of cardiovascular disease. One method includes receiving a patient-specific vessel model and a patent-specific tissue model of a patient anatomy; extracting one or more patient-specific physiological parameters (e.g. blood flow, anatomical characteristics, image characteristics, etc.) from the vessel or tissue models for one or more physiological states of the patient; estimating a characteristic of the perfusion of the patient-specific tissue model (e.g., via a trained machine learning algorithm) using the patient-specific physiological parameters; and outputting the estimated perfusion characteristic to a display.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Application No. 62/142,158, filed Apr. 2, 2015, the contentsof which are hereby incorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure relate generally todisease assessment, treatment planning, and related methods. Morespecifically, particular embodiments of the present disclosure relate tosystems and methods for estimating perfusion of a target tissue.

BACKGROUND

Coronary artery disease is a common ailment that affects millions ofpeople. Coronary artery disease may cause the blood vessels providingblood to the heart to develop lesions, such as a stenosis (abnormalnarrowing of a blood vessel). As a result, blood flow to the heart maybe restricted. A patient suffering from coronary artery disease mayexperience chest pain, referred to as chronic stable angina duringphysical exertion or unstable angina when the patient is at rest. A moresevere manifestation of disease may lead to myocardial infarction, orheart attack. Significant strides have been made in the treatment ofcoronary artery disease including both medical therapy (e.g. statins) orsurgical alternatives (e.g., percutaneous coronary intervention (PCI)and coronary artery bypass graft surgery (CABG)). Invasive assessmentsare commonly used to assess the type of treatment a patient may receive.However, indirect or noninvasive assessments for formulating a patienttreatment are being explored and developed.

Heart disease is typically viewed as resulting from vessel disease, inparticular, narrowing of the vessels or blockage inside vessel lumens ina way that impacts blood flow. One way to measure the extent of thisnarrowing or blockage is through a perfusion scan, since perfusion isthe flow of blood through a vascular network (e.g., arteries,capillaries, etc). Currently, perfusion scans may be costly and mayexpose the patient to unnecessary radiation. Thus, a desire exists touse available patient information to estimate perfusion in certaintarget tissue, where the estimated perfusion data may be used tosimulate a familiar scan type, for example, single positron emissioncomputed tomography (SPECT) or positron emission tomography (PET), sothat a physician may be familiar with how to read the image.Furthermore, a desire exists to improve treatment of cardiovasculardisease by better assessing the severity of cardiovascular disease.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for using available information to estimateperfusion of a target tissue to guide diagnosis or treatment ofcardiovascular disease.

One method includes: receiving a patient-specific vessel model and apatent-specific tissue model of a patient's anatomy; extracting one ormore patient-specific physiological parameters of the patient from thevessel or tissue models at one or more physiological states of thepatient; estimating a characteristic of the perfusion of blood throughthe patient-specific tissue model using the patient-specificphysiological parameters; and outputting the estimated characteristic ofthe perfusion of blood to a display.

In accordance with another embodiment, system for estimatingpatient-specific perfusion, the system comprising: a data storage devicestoring instructions for determining patient-specific characteristics ofthe perfusion of blood; and a processor configured to execute theinstructions to perform a method including the steps of: receiving apatient-specific vessel model and a patent-specific tissue model of apatient's anatomy; extracting one or more patient-specific physiologicalparameters of the patient from the vessel or tissue models at one ormore physiological states of the patient; estimating a characteristic ofthe perfusion of blood through the patient-specific tissue model usingthe patient-specific physiological parameters; and outputting theestimated characteristic of the perfusion of blood to a display.

In accordance with another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for estimatingpatient-specific characteristics of the perfusion of blood, the methodcomprising: receiving a patient-specific vessel model and apatent-specific tissue model of a patient's anatomy; extracting one ormore patient-specific physiological parameters of the patient from thevessel or tissue models at one or more physiological states of thepatient; estimating a characteristic of the perfusion of blood throughthe patient-specific tissue model using the patient-specificphysiological parameters; and outputting the estimated characteristic ofthe perfusion of blood to a display.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages on the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the detailed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments,and together with the description, serve to explain the principles ofthe disclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network forpredicting perfusion to guide diagnosis or treatment of cardiovasculardisease, according to an exemplary embodiment of the present disclosure.

FIG. 2 is a block diagram of an general method of estimating perfusion,according to a general embodiment of the present disclosure.

FIG. 3 is a block diagram of an exemplary method of estimatingperfusion, according to an exemplary embodiment of the presentdisclosure.

FIG. 4 is a block diagram of an exemplary method of estimatingperfusion, using machine learning, according to an exemplary embodimentof the present disclosure. FIG. 4 may also disclose a method ofperforming steps 208 or 322 in FIG. 2 and FIG. 3, respectively, fordetermining an estimate of tissue perfusion.

FIG. 5 is a block diagram of the method disclosed in FIG. 4 in greaterdetail. Furthermore, FIG. 5 also discloses an exemplary method forestimating patient-specific perfusion characteristics from vesselgeometry and patient-specific physiological parameters (e.g., anatomicalinformation, secondary information, and blood supply information), usingmachine learning, according to an exemplary embodiment of the presentdisclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

Coronary artery disease is a common ailment, by which blood flow to theheart may be restricted. While significant strides have been made in thetreatment of coronary artery disease, the treatment is often misplacedor excessive. For example, patients often undergo invasive surgicaltreatments, or perfusion scans which may be costly and/or expose thepatient to unnecessary radiation. Patients are sometimes subjected totreatments that may not change their condition. In some situations,patients even undergo treatments that ultimately worsen their condition.Thus, a need exists to accurately assess the severity of cardiovasculardisease and/or predict perfusion to aid in selecting a course oftreatment.

Cardiovascular disease may be linked to vessel disease, meaning vesselnarrowing or blockage. A cardiac perfusion scan may measure the amountof blood in the heart muscle at different physiological states. A“physiological state” may refer to a resting patient state, a hyperemicstate, an exercise state, a postprandial state, a gravitational state,an emotional state, a state of hypertension, a medicated state or acombination thereof. A perfusion scan is often performed to determinewhat may be causing chest pain and to determine if the tissue of theheart is supplied with an adequate flow of blood, or to determine howmuch heart muscle has been damaged from the heart attack.

During the scan, images of the heart are generated after a radioactivetracer is intravenously administered to the patient. The radioactivetracer travels through the blood and into the heart muscle. As thetracer moves through the heart muscle, tissues that have sufficientblood flow absorb the tracer. Tissue that does not absorb the tracer maynot receive enough blood or may have been damaged by a heart attack. Twosets of images may be generated during a cardiac perfusion scan. The atrest images are then compared with the stress or non-rest images and alevel of perfusion in the target tissue may be determined. A “targettissue” may refer to a tissue and/or organ in which the blood supplyand/or perfusion characteristics may be estimated.

Therefore, an understanding of perfusion in the target tissue may beclinically important. An understanding of perfusion may improve anevaluation of the severity of disease and of the appropriateness oftreatment. The present disclosure may benefit patients and doctors byeither estimating perfusion under conditions in which perfusion may bedifficult to measure, and/or by employing measurements of cardiacperfusion to more accurately assess the severity of vessel disease indifferent physiological conditions. The perfusion images generated maysimulate images generated from a familiar scan type such as PET and/orSPECT. The simulated images may provide ease of reading andunderstanding to physicians who are trained to read PET and/or SPECTimages. For the purposes of the disclosure: “patient” may refer to anyindividual or person for whom diagnosis or treatment analysis is beingperformed, or any individual or person associated with the diagnosis ortreatment analysis of one or more individuals.

While FIG. 1 provides an abstract view of the system and network of thecurrent disclosure, FIG. 2 illustrates a general embodiment of a methodfor estimating one or more characteristics of the perfusion of blood,and FIG. 3 lays out a more specific embodiment. Furthermore, both FIG.2, and FIG. 3 disclose the step of determining an estimate of theperfusion of blood through a tissue. FIG. 4 illustrates an exemplarymethod for performing the step of determining an estimate of one or morecharacteristics of the perfusion of blood through a tissue using atrained machine learning algorithm. FIG. 5 examines the method disclosedin FIG. 4 in further detail.

Referring now to the figures in more detail, FIG. 1 depicts a blockdiagram of an exemplary system 100 and network for estimating cardiacperfusion to guide diagnosis or treatment of cardiovascular disease,according to an exemplary embodiment. Specifically, FIG. 1 depicts aplurality of physicians 102 and third party providers 104, any of whommay be connected to an electronic network 101, such as the Internet,through one or more computers, servers, and/or handheld mobile devices.Physicians 102 and/or third party providers 104 may create or otherwiseobtain images of one or more patients' anatomy. The physicians 102and/or third party providers 104 may also obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, patient activity, or exercise level, etc.Physicians 102 and/or third party providers 104 may transmit theanatomical images and/or patient-specific information to server systems106 over the electronic network 101. Server systems 106 may includestorage devices for storing images and data received from physicians 102and/or third party providers 104. Server systems 106 may also includeprocessing devices for processing images and data stored in the storagedevices.

FIG. 2 depicts a general embodiment of an exemplary method 200 forestimating cardiac perfusion to guide diagnosis or treatment ofcardiovascular disease. The method of FIG. 2 may be performed by serversystems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network101.

In one embodiment, step 202 may include receiving a patient-specificvessel model of a patient's anatomy and a patient-specific target tissuemodel stored in an electronic storage medium of the storage system 106.An “electronic storage medium” may include, but is not limited to, ahard drive, network drive, cloud drive, mobile phone, tablet, or thelike that may or may not be affixed to a display screen. Specifically,receiving the patient-specific vessel model and the patient-specifictarget tissue model may include either generating the patient-specificvessel model and/or the patient-specific target tissue model at theserver system 106, or receiving the patient-specific vessel model and/orthe patient-specific target tissue model over an electronic network(e.g., electronic network 101). The patient-specific vessel model andthe patient-specific target tissue model may include a cardiovascularmodel of a specific person. In one embodiment, the vessel model and thetarget tissue model may be derived from images of the person acquiredvia one or more available imaging or scanning modalities (e.g., computedtomography (CT) scans and/or magnetic resonance imaging (MR)). Forexample, step 202 may include receiving CT and/or MR images of aperson's heart. Step 202 may further include generating, from thereceived images, a patient-specific cardiovascular model for theparticular person. The electronic storage medium may include, but is notlimited to, a hard drive, network drive, cloud drive, mobile phone,table, or the like.

In one embodiment, steps 204 and 206 may include receiving orcalculating one or more patient-specific physiological parameters. Thesepatient-specific physiological parameters may be received or calculatedfrom the received vessel model and/or target tissue model. Thesepatient-specific physiological parameters may include anatomicalcharacteristics, image characteristics, as well as secondarycharacteristics related to the patient and/or the patient's anatomy(e.g., patient characteristics, disease burden characteristics, andelectromechanical measurements). The patient-specific physiologicalparameters may also include parameters related to blood circulation,including an estimation of the blood supply to each area of a targettissue and/or blood flow characteristics, under one or morephysiological states.

Specifically, step 204 may include receiving or calculating one or moreanatomical characteristics, image characteristics, patientcharacteristics, disease burden characteristics, and/orelectromechanical characteristics, under one or more physiologicalstates. One instance of a physiological state may be a resting state.Another physiological state may be a physiological state other than theresting state, or an “active” physiological state. Active physiologicalstates may include hyperemia, various levels of exercise, post prandial,positional (e.g., supine-upright), gravitational (e.g. G-forces, zerogravity, etc.), or a combination thereof. In one embodiment, thepatient-specific physiological parameters may be obtained from sourcesother than the vessel model and/or target tissue model.

In one embodiment, step 206 may include receiving or calculating anestimated supplied blood to each area of a target tissue or to eachvessel in a vascular network and/or estimated blood flowcharacteristics, under one or more physiological states. Theseestimations may be based on a measurement (e.g., by measuring throughimaging) or via an estimation of supplied blood in a resting state(e.g., based on a 3D simulation, a 1D simulation, or a learnedrelationship).

In one embodiment, step 208 may include determining and outputting anestimation of perfusion in the vessel of the vessel model or area in thetarget tissue model, using joint prior information. The joint priorinformation may refer to the one or more received patient-specificphysiological parameters (e.g., received or calculated medical imagecharacteristics, anatomical characteristics, blood supply to the targettissue, blood flow characteristics, patient characteristics, diseaseburden characteristics, electromechanical measurements, etc.) determinedin steps 204 and 206. In one embodiment, determining an estimation ofperfusion may involve determining an estimation of supplied blood at oneor more vessel locations of the person's vessel model, while the personis in a given physiological state. This determination may also be basedon a measurement of blood flow (e.g., by imaging) or via an estimationof blood flow in a resting state (e.g., based on a 3D simulation, a 1Dsimulation, or a learned relationship). In one embodiment, step 208 mayinclude calculating an estimation of the perfusion territories of thetarget tissue related to the vascular model. This estimation may bedetermined, by using a nearest-neighbor (e.g., Voronoi diagram) approachto assign locations in the target tissue to the closest supplying vesselin the vascular model. The estimation may also be determined using amicrovascular estimation technique from an anatomical model, forexample, by using a constrained constructive optimization approach. Inone embodiment, step 208 may be performed by a processor. The processormay estimate perfusion at one or more locations of the target tissue inthe vascular model in one or more psychological states by machinelearning. In one embodiment, step 208 may further include outputting theestimation of perfusion to an electronic storage medium (e.g., harddisk, network drive, portable disk, smart phone, tablet etc.) and/or toa display screen. In one embodiment, the output perfusion estimates maybe displayed in greyscale or color in 2D or 3D. The calculated perfusionestimates may be overlaid on the anatomical model of the target tissueand/or overlaid on an image of the target tissue.

In one embodiment, step 210 may include estimating virtual perfusiondesigned to simulate a SPECT or a PET scan in one or more physiologicalstates. In one embodiment, the estimation may be performed by modelingcontrast agent in the concentrations given by the perfusion estimates.In another embodiment, the said estimation may involve performing aMonte Carlo simulation to estimate the collimation of photons orpositrons at virtual collimator locations. Using the collimatorestimation, a SPECT or PET image may be reconstructed using standardtomographic techniques. The estimated virtual perfusion image may besaved to an electronic storage medium and/or displayed on a monitor.

FIG. 3 depicts an exemplary embodiment of method 300 for estimatingcardiac perfusion to guide diagnosis or treatment of cardiovasculardisease. The method of FIG. 3 may be performed by server systems 106,based on information, images, and data received from physicians 102and/or third party providers 104 over electronic network 101.

In one embodiment, step 302 may include receiving a patient-specificvascular model of at least the target tissue of a patient anatomy in anelectronic storage medium of the storage system 106. Specifically,receiving the patient-specific anatomic model may include eithergenerating the patient-specific anatomical model at the server system106, or receiving one over an electronic network (e.g., electronicnetwork 101). The patient-specific anatomic model may include acardiovascular model of a specific person. In one embodiment, theanatomic model may be derived from images of the person acquired via oneor more available imaging or scanning modalities (e.g., CT scans and/ormagnetic resonance imaging). For example, step 302 may include receivingCT and/or MRI images of a person's heart. Step 302 may further includegenerating, from the received images, a patient-specific cardiovascularmodel for the particular person. The electronic storage medium mayinclude, but is not limited to, a hard drive, network drive, clouddrive, mobile phone, tablet, or the like.

In one embodiment, step 304 may include calculating an estimation of oneor more of the anatomical characteristics of the target tissue. Theanatomical characteristics may include, but are not limited to, vesselsize, vessel shape, tortuosity, thickness, and/or estimated perfusionterritories within a target tissue or vascular network. This calculationmay be based on a measurement (e.g., by measuring the anatomicalcharacteristics from imaging) or via an estimation of the anatomicalcharacteristics in a resting state (e.g., based on a 3D simulation, a 1Dsimulation, or a learned relationship).

In one embodiment, step 306 may include receiving one or more imagecharacteristics of the target tissue. The image characteristics may beobtained from CT scan images, MRI images, ultrasound images, PET images,or SPECT images. The images may capture the vascular model in one ormore physiological states (e.g., rest, stress, active). The imagecharacteristics of the target tissue or vessels may be received orcalculated in one or more locations of the vascular model. The imagecharacteristics may include, but are not limited to, local averageintensities at one or more image resolutions, differences of the averageintensities (e.g., calculated via wavelet bases, using for example, Haarwavelets), texture characteristics (e.g., Haralick texture features),and any standard image features including histograms, gradients, SIFT,or steerable filters etc.

In one embodiment, step 308 may include receiving patientcharacteristics. The patient characteristics may include, but are notlimited to, age, gender, smoking history, height, weight, diabeticstatus, hypertensive status, ethnicity, family history, blood type,prior history of drug use, and/or genetic history. The patientcharacteristics may be obtained via the electronic network 100 or fromthe patient's physician 102 or from a third party provider 103.

In one embodiment, step 310 may include receiving the vascular or targettissue disease characteristics. The target tissue diseasecharacteristics may include, but are not limited to, the presence andextent of plaque buildup within the arteries, the presence of plaquecharacteristics (e.g., spotty calcification, low attenuation plaque,napkin-ring sign, positive remodeling), patient level or vessel levelcalcium scores, tissue viability information, vessel wall motion, vesselwall thickness, and/or ejection fraction information.

In one embodiment, step 312 may include receiving electromechanicalmeasurements. The electromechanical measurements may include, but arenot limited to, electrocardiography (ECG) measurements, or invasiveelectrophysiology (EP) measurements.

In one embodiment, step 314 may include calculating an estimation of thesupplied blood to each area of the target tissue under one or morephysiological states. One instance of a first physiological state may bea resting state. This calculation may be based on a measurement (e.g.,by measuring through imaging) or via an estimation of supplied blood ina resting state (e.g., based on a three-dimensional (3D) simulation, aone-dimensional (1D) simulation, or a learned relationship). Anotherphysiological state may be a physiological state other than the restingstate, or an “active” physiological state. One instance of such aphysiological state may include hyperemia. Other non-restingphysiological states may include, various levels of exercise, postprandial, positional (e.g., supine-upright), gravitational (e.g.G-forces, zero gravity, etc.).

In one embodiment, step 316 may include calculating one or more bloodflow characteristics of the target tissue. In one embodiment, the bloodflow characteristics may include, but is not limited to, a fractionalflow reserve value (FFR), flow direction, and/or flow magnitude and isdetermined by an estimation of blood flow to the target tissue. In oneembodiment, the blood flow characteristic may be calculated by severalmeans, including, but not limited to, invasive measurements (e.g.,invasive FFR, thrombosis in myocardial infarction (TIMI), ormicrospheres), calculation using a blood flow simulation model (e.g., a3D or 1D fluid simulation model, calculation, or TAFE), calculationusing imaging characteristics (e.g., TAG or CCO), or calculation using amachine learning estimation of blood supply based on anatomical orimaging features. In one embodiment, step 316 may include calculating anestimation of the blood flow in the perfusion territories of the targettissue related to the vascular model. This estimation may be determinedby using a nearest-neighbor (e.g., Voronoi diagram) approach toassigning locations in the target tissue to the closest supplying bloodvessel in the vascular model. The estimation may also be determinedusing a microvascular estimation technique from an anatomical model, forexample, by using a constrained constructive optimization approach. Inone embodiment, step 316 may be performed by a processor. The processormay estimate perfusion at one or more locations of the target tissue inthe vascular model in one or more psychological states by machinelearning.

In one embodiment, step 318 may include calculating, using a processor,an estimate of the perfusion at one or more locations in the targettissue related to the vascular model. The estimation of perfusion may becalculated for one or more physiological states using the one or more ofthe patient specific patient-specific physiological parameters (e.g.,image characteristics, anatomical characteristics, estimated bloodsupply, estimated blood flow characteristics, estimated perfusionterritories, patient characteristics, disease burden characteristics,and/or electromechanical measurements). In one embodiment, thiscalculation may be performed by training a machine learning algorithmusing a database of patients with known perfusion characteristics andknown patient-specific physiological parameters, including, but notlimited to, the image characteristics, anatomical characteristics,estimated perfusion territories, disease burden characteristics, and/orelectromechanical measurements. In one embodiment step 318 may beperformed using a processor.

In one embodiment, step 320 may include outputting the estimation ofperfusion to an electronic storage medium (e.g., hard disk, networkdrive, portable disk, smart phone, tablet etc.) and/or to a displayscreen. In one embodiment, the output perfusion estimates may bedisplayed in greyscale or color in 2D or 3D. In one embodiment, theoutput perfusion estimates may be overlaid or superimposed on theanatomical model of the target tissue and/or overlaid or superimposed onan image of the target tissue. In one embodiment, this determination maybe performed by training a machine learning algorithm using a databaseof patients with known perfusion characteristics and knownpatient-specific physiological parameters. In one embodiment, theperfusion estimates may be used to estimate a virtual perfusion imagedesigned to simulate a SPECT or PET image in one or more of thephysiological states of the patient. In one embodiment, the virtualperfusion image may be saved into an electronic storage medium and/oroutput to a display. The estimation of the virtual perfusion image maybe performed by modeling a contrast image in the concentrations given bythe perfusion estimates, performing a Monte Carlo simulation to estimatethe collimation of photons or positrons at a plurality of virtualcollimator locations, and/or using the collimator estimation toreconstruct a SPECT or PET image using standard tomographic techniques.The estimated virtual perfusion image may be similar in readability anddesign to a SPECT or PET scan image, and thus the physician may befamiliar with how to analyze the estimated perfusion image.

The above recited steps of methods 200 and 300 may be used to estimateperfusion in a variety of tissues, including, but not limited to, themyocardium using a coronary vascular model, the brain using a cerebralvascular model, muscle tissue using a peripheral vascular model, theliver using a hepatic vascular model, the kidney using a renal vascularmodel, the bowel using a visceral vascular model, and in other organsincluding the spleen and pancreas, using a vascular model for vesselssupplying blood to the target organ.

In one embodiment, the perfusion estimation may also be used to enhancea blood flow simulation by using more accurate boundary conditions toperform a simulation or estimation of blood flow characteristics.

In one embodiment, treatment planning and diagnosis may be improved byvirtually changing the input information (e.g. the vascular model,tissue model, patient-specific physiological parameters, etc.) andpredicting the effects on perfusion in the target tissue based on thechanged inputs.

FIG. 4 depicts an exemplary embodiment of method 400 for training amachine learning algorithm to determine an estimate of perfusion at oneor more locations in the target tissue in one or more physiologicalstates. The method of FIG. 4 may be performed by server systems 106,based on information, images, and data received from physicians 102and/or third party providers 104 over electronic network 101.

In one embodiment, step 402 may include assembling a database containingone or more of the patient-specific physiological parameters at one ormore locations in the vascular and/or target tissue model and theestimated or measured perfusion data those locations. The“patient-specific physiological parameters” may refer to one or more ofthe received or calculated medical image characteristics, anatomicalcharacteristics, perfusion territories, blood supply to the targettissue, blood flow characteristics, patient characteristics, diseaseburden characteristics, and/or electromechanical measurements. Thelocations may be from the patient-specific vascular and/or target tissuemodels, or images obtained from one or more available imaging orscanning modalities, including, but not limited to, PET, SPECT, MRperfusion, and/or CT perfusion.

In one embodiment, step 404 may include training a machine learningalgorithm to map the one or more patient-specific physiologicalparameters at one or more locations of the vascular and/or target tissuemodel to the perfusion data at those locations. The machine learningalgorithm may take many forms, including, but not limited to, amulti-layer perceptron, deep learning, support vector machines, randomforests, k-nearest neighbors, Bayes networks, etc.

In one embodiment, step 406 may include applying the trained machinelearning algorithm to the set of patient-specific physiologicalparameters of the vascular model and/or target tissue obtained from anew patient to estimate the perfusion data at one or more locations.

FIG. 5 is a block diagram of an exemplary method for estimatingpatient-specific blood flow characteristics from vessel geometry andphysiological information, according to an exemplary embodiment of thepresent disclosure. The method of FIG. 5 may be performed by serversystems 106, based on information received from physicians 102 and/orthird party providers 104 over electronic network 100.

In one embodiment, the method of FIG. 5 may include a training method502, for training one or more machine learning algorithms based onpatient-specific physiological parameters from numerous patients andmeasured or estimated perfusion characteristics, and a production method504 for using the machine learning algorithm results to predict aparticular patient's perfusion characteristics.

In one embodiment, training method 502 may involve acquiring, for eachof a plurality of individuals, e.g., in digital format: (a) apatient-specific geometric model, (b) one or more measured or estimatedpatient-specific physiological parameters, and (c) values of perfusioncharacteristics. Training method 502 may then involve, for one or morepoints in each patient's model, creating a feature vector of thepatients' physiological parameters and associating the feature vectorwith the values of perfusion characteristics. Training method 1002 maythen train a machine learning algorithm (e.g., using processing devicesof server systems 106) to predict perfusion at each point of a geometricmodel, based on the feature vectors and estimated perfusioncharacteristics. Training method 502 may then save the results of themachine learning algorithm, including feature weights, in a storagedevice of server systems 106. The stored feature weights may define theextent to which patient-specific physiological parameters and/oranatomical geometry are predictive of certain perfusion characteristics.

In another embodiment, training method 502 may be performed based on FFRestimates generated using computational fluid dynamics (CFD) techniquesfor a plurality of patients. Training method 502 may then involveassociating an estimated FFR value with every point in a patient'sgeometric model, and then creating a feature vector of thepatient-specific physiological parameters and associating the featurevector with FFR estimates. Training method 502 may then train a machinelearning algorithm (e.g., using processing devices of server systems106) to predict perfusion at each point of a geometric model, based onthe feature vectors and estimated FFR.

In one embodiment, the production method 504 may involve estimatingperfusion characteristics for a particular patient, based on results ofexecuting training method 502. In one embodiment, production method 504may include acquiring, e.g. in digital format: (a) a patient-specificgeometric model, and (b) one or more measured or estimatedpatient-specific physiological parameters. For multiple points in thepatient's geometric model, production method 504 may involve creating afeature vector of the patient-specific physiological parameters used inthe training mode. Production method 504 may then use saved results ofthe machine learning algorithm to produce estimates of the patient'sperfusion characteristics for each point in the patient-specificgeometric model. Finally, production method 504 may include saving theresults of the machine learning algorithm, including predicted perfusioncharacteristics, to a storage device of server systems 106.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method for estimatingpatient-specific blood perfusion to guide diagnosis or treatment ofcardiovascular disease, the method comprising: receiving measured dataof a characteristic of perfusion of blood through tissues of a pluralityof individuals; receiving measured data of one or more anatomical orphysiological parameters for each of the plurality of individuals;generating a mapping of the one or more anatomical or physiologicalparameters to the characteristic of perfusion of blood, by training amachine learning algorithm to learn the mapping using a training setcomprising the measured data of the characteristic of perfusion of bloodand the measured data of the one or more anatomical or physiologicalparameters; generating, from image data of a patient, a patient-specificvessel model of a vessel of the patient and a patient-specific tissuemodel of a target tissue perfused by the vessel; extracting, from thepatient-specific vessel model or the patient-specific tissue model, oneor more patient-specific values of the one or more anatomical orphysiological parameters; computing an estimate of the characteristic ofperfusion of blood through the patient-specific tissue model using themapping, by inputting the one or more patient-specific values of the oneor more anatomical or physiological parameters into the trained machinelearning algorithm to obtain the estimate as an output of the trainedmachine learning algorithm; and generating a virtual scan comprising thecomputed estimate of the characteristic of perfusion of blood.
 2. Thecomputer implemented method of claim 1, wherein the one or moreanatomical or physiological parameters include an estimated or measuredblood flow to a plurality of regions of a vessel or tissue at one ormore physiological states.
 3. The computer implemented method of claim1, wherein the one or more anatomical or physiological parametersinclude one or more anatomical geometries of: vessel size, vessel shape,vessel tortuosity, vessel length, vessel thickness, estimatedterritories of the perfusion of blood within a tissue or vascularnetwork, or a combination thereof.
 4. The computer implemented method ofclaim 1, wherein the one or more patient-specific values of the one ormore anatomical or physiological parameters characterize the patient inone or more physiological states, and the one or more physiologicalstates include one or more of: a resting patient state, a hyperemicstate, an exercise state, a postprandial state, a gravitational state,an emotional state, a state of hypertension, a medicated state, or acombination thereof.
 5. The computer implemented method of claim 1,wherein the characteristic of perfusion of blood includes one or more ofa fractional flow reserve, flow magnitude, flow direction, or acombination thereof.
 6. The computer implemented method of claim 1,wherein the one or more anatomical or physiological parameters includeone or more image characteristics of a target tissue or a vessel in oneor more physiological states, including one or more of local averageintensities, texture characteristics, standard image, or a combinationthereof.
 7. The computer implemented method of claim 1, wherein the oneor more anatomical or physiological parameters include one or more of:patient characteristics, target tissue disease characteristics,electromechanical measurements, or a combination thereof.
 8. Thecomputer implemented method of claim 1, wherein the computing theestimate of the characteristic of perfusion of blood through thepatient-specific tissue model includes comparing the blood flow in thetarget tissue at one or more physiological states.
 9. The computerimplemented method of claim 1, wherein the patient-specific vessel modeland the patient-specific tissue model includes one or more of: acoronary vasculature and the myocardium; a cerebral vasculature and thebrain; a peripheral vasculature and muscle; a hepatic vasculature and aliver; a renal vasculature and a kidney; a visceral vasculature and abowel; or any target organ and a vasculature supplying blood to saidtarget organ.
 10. The computer implemented method of claim 1, furthercomprising: adjusting the one or more patient-specific values of the oneor more physiological parameters based on the estimate of thecharacteristic of perfusion of blood; and simulating a blood flowcharacteristic using the estimate and the adjusted one or morepatient-specific values of the one or more anatomical or physiologicalparameters.
 11. The computer implemented method of claim 1, furthercomprising: receiving one or more desired perfusion characteristics atone or more locations in the target tissue; comparing the estimate ofthe characteristic of perfusion of blood at the one or more locations inthe target tissue with the one or more desired perfusion characteristicsat the one or more locations; and changing one or more of the one ormore patient-specific values of the one or more anatomical orphysiological parameters or the patient-specific tissue model, based onthe comparison.
 12. The computer implemented method of claim 1, whereinthe virtual scan comprises the computed estimate of the characteristicperfusion of blood overlaid on an image of the target tissue to therebysimulate a SPECT or PET scan in one or more physiological states, andthe method further comprises outputting the one or more virtualperfusion images to an electronic storage medium.
 13. The method ofclaim 1, further comprising, adjusting the one or more patient-specificvalues of the one or more anatomical or physiological parameters basedon the estimate of the characteristic of the perfusion of blood; andrestoring blood flow through the patient-specific tissue model using theadjusted one or more patient-specific values of the one or moreanatomical or physiological parameters.
 14. A system for estimatingpatient-specific blood perfusion to guide diagnosis or treatment ofcardiovascular disease, the system comprising: a data storage devicestoring instructions for determining patient-specific characteristics ofthe perfusion of blood; and a processor configured to execute theinstructions to perform a method including the steps of: receivingmeasured data of a characteristic of perfusion of blood through tissuesof a plurality of individuals; receiving measured data of one or moreanatomical or physiological parameters for each of the plurality ofindividuals; generating a mapping of the one or more anatomical orphysiological parameters to the characteristic of perfusion of blood, bytraining a machine learning algorithm to learn the mapping using atraining set comprising the measured data of the characteristic ofperfusion of blood and the measured data of the one or more anatomicalor physiological parameters; generating, from image data of a patient, apatient-specific vessel model of a vessel of the patient and apatient-specific tissue model of a target tissue perfused by the vessel;extracting, from the patient-specific vessel model or thepatient-specific tissue model, one or more patient-specific values ofthe one or more anatomical or physiological parameters; computing anestimate of the characteristic of perfusion of blood through thepatient-specific tissue model using the mapping, by inputting the one ormore patient-specific values of the one or more anatomical orphysiological parameters into the trained machine learning algorithm toobtain the estimate as an output of the trained machine learningalgorithm; and generating a virtual scan comprising the computedestimate of the characteristic of perfusion of blood.
 15. The system ofclaim 14, wherein the one or more anatomical or physiological parametersinclude, at one or more physiological states, one or more of thefollowing: an estimated or measured blood flow to a plurality of regionsof a vessel or tissue; anatomical characteristics; imagecharacteristics; patient characteristics; target tissue diseasecharacteristics; electromechanical measurements; or a combinationthereof.
 16. The system of claim 14, wherein the one or morepatient-specific values of the one or more anatomical or physiologicalparameters characterize the patient in one or more physiological states,and the one or more physiological states include, one or more of: aresting patient state, a hyperemic state, an exercise state, apostprandial state, a gravitational state, an emotional state, a stateof hypertension, a medicated state, or a combination thereof.
 17. Thesystem of claim 14, wherein the characteristic of the perfusion of bloodinclude one or more of: fractional flow reserve, flow magnitude, flowdirection, or a combination thereof.
 18. The system of claim 14, whereinthe computing the estimate of the a characteristic of perfusion of bloodthrough the patient-specific tissue model includes comparing the bloodflow in the target tissue at one or more physiological states.
 19. Thesystem of claim 14, wherein the patient-specific vessel model and thepatient-specific tissue model includes one or more of: a coronaryvasculature and the myocardium; a cerebral vasculature and the brain; aperipheral vasculature and muscle; a hepatic vasculature and a liver; arenal vasculature and a kidney; a visceral vasculature and a bowel; orany target organ and a vasculature supplying blood to said target organ.20. The system of claim 14, wherein the method further comprises:adjusting the one or more patient-specific values of the one or moreanatomical or physiological parameters based on the estimate of thecharacteristic of the perfusion of blood; and simulating a blood flowcharacteristic using the estimate of the characteristic of bloodperfusion and the adjusted one or more patient-specific values of theone or more anatomical or physiological parameters.
 21. A non-transitorycomputer readable medium for performing a method on a computer systemcontaining computer-executable programming instructions for estimatingpatient-specific blood perfusion to guide diagnosis or treatment ofcardiovascular disease, the method comprising: receiving measured dataof a characteristic of perfusion of blood through tissues of a pluralityof individuals; receiving measured data of one or more anatomical orphysiological parameters for each of the plurality of individuals;generating a mapping of the one or more anatomical or physiologicalparameters to the characteristic of perfusion of blood, by training amachine learning algorithm to learn the mapping using a training setcomprising the measured data of the characteristic of perfusion of bloodand the measured data of the one or more anatomical or physiologicalparameters; generating, from image data of a patient, a patient-specificvessel model of a vessel of the patient and a patient-specific tissuemodel of a target tissue perfused by the vessel; extracting, from thepatient-specific vessel model or the patient-specific tissue model, oneor more patient-specific values of the one or more anatomical orphysiological parameters; computing an estimate of the characteristic ofperfusion of blood through the patient-specific tissue model using themapping, by inputting the one or more patient-specific values of the oneor more anatomical or physiological parameters into the trained machinelearning algorithm to obtain the estimate as an output of the trainedmachine learning algorithm; and generating a virtual scan comprising thecomputed estimate of the characteristic of perfusion of blood.